Causal inference

Epidemiologists need to work diligently to weed out possible bias, in the selection of subjects for study and in the collection of data on exposure to possible causative factors. If, in a given study, an association is found, but bias is identified and cannot be accounted for, it may be that causal inferences can simply never be drawn. 

Newman MC, Evans DA. 2002. Causal inference in risk assessments cognitive idols or Bayesian theory In Newman MC, Roberts M, Hale R, editors. Coastal and estuarine risk assessment. Boca Raton (FL) CRC Press, p 73-96. 

The developmental neurotoxicity guideline, accepted by OECD in 2007, has added the important aspect of behavioral effects of pre- and postnatal exposure to chemicals. This development arose from the notion that behavioral disorders in man such as anxiety, depression, phobias, autism, and attention deficit hyperactivity disorder, which appear to show increasing prevalences in western societies, may have a perinatal origin (4, 5). In the absence of causal inferences with respect to chemicals it seems nevertheless prudent to assess in a risk assessment whether such causal relations may exist. 

Susser M (1977) Judgement and causal inference criteria in epidemiologic studies. Am J Epidemiol, 105(1) 1-15. 

Fox GA. 1991. Practical causal inference for ecoepidemiologists. J Toxicol Environ Hlth 33 359-373. 

TABLE C-2 Epidemiological Considerations Important for Causal Inference (1959-1973)... 

This is the classic randomised controlled trial (RCT), the most secure method for drawing a causal inference about the effects of treatments. Randomisation attempts to control biases of various kinds when assessing the effects of treatments. RCTs are employed at all phases of drug development and in the various types and designs of trials discussed below. 

Data from the Third National Health and Nutrition Survey (1988-94) have been used to analyse the possible effects of DTP or tetanus immunization on allergies and allergy-related sjmptoms among 13 944 infants, children, and adolescents aged 2 months to 16 years in the USA (6). The authors concluded that DTP or tetanus immunization increases the risk of allergies and related respiratory symptoms in children and adolescents. However, the small number of non-immunized individuals and the study design limited their abihty to make firm causal inferences about the true magnitude of effect. 

In a cohort study of 552 patients with acute renal insufficiency studied from 1989 to 1995 diuretic use was associated with a significant increase in the risk of death or non-recovery of renal function (33). This increased risk was largely borne by patients who were relatively unresponsive to diuretics. Although this study was observational, which prohibits causal inference, it is unlikely that diuretics afford any material benefit in the setting of acute renal insufficiency. 

Several mistakes of inference should be avoided by those attempting to assign cause-and-effect relationships. For example, although anecdotes and case reports can suggest testable hypotheses, they should not, by themselves, provide a basis for causal inference, especially in the absence of unbiased selection of subjects, examination of patients for other explanations of the adverse event, and measurement of the frequency of the same adverse event in appropriate control patients. 

The need to control the experiment-wise error rate may not apply to exploratory analyses. Statisticians often perform formal statistical tests for exploratory purposes. So, no formal hypotheses are stated and no inferences are made based on them. Even though the act of performing formally an exploratory test involves the same steps as inferential testing, it is conceptually different because of the absence of a null hypothesis. The p-value obtained in such a test should be viewed as a measure of the level of inconsistency of the data with the underlying assumptions of the test rather than error probabilities involved in making causal inferences. 

Rothman, K. J., and Greenland, S. (2005). Causation and causal inference in epidemiology. Am J Pub Health 95 (Suppl 1), S144-S150. 

Weed, D. L., and Gorelic, L. S. (1996). The practice of causal inference in cancer epidemiology. Cancer Epidemiol Biomarkers Prev 5, 303-311. 

A major source of confusion in the media relates to how science tries to separate unavoidable death due to natural causes from avoidable death due to modem drugs or chemicals. This is the field of risk assessment and is a crucial function of our regulatory agencies. Confusion and unnecessary anxiety arise from the complexity of the underlying science, misinterpretation of the meaning of scientific results, and a lack of understanding of the basic principles of probability, statistics, and causal inference (assigning cause and effect to an event). 

Epidemiology is an observational science and, therefore, causal inference involves a somewhat different approach to that which is generally used in laboratory-based sciences. Since the epidemiologist cannot usually conduct controlled experiments,... 

The results of this analysis can be used for policy and planning purposes to show the incremental benefit of AP over and above the contribution of SP. This analysis has fully exploited published data to measure the likely contribution of AP and SP in Italy between the 1970s and 1990s. The cross-sectional and longitudinal surveys have provided a valid base to produce a method that can help policy makers to advocate the extra benefits of AP. Even if this natural experiment cannot produce the same evidence as a randomized community trial, the causal inference between the increase in urinary iodine and the decline in goiter with and without AP is clear, and the incremental cost-effectiveness of AP can be estimated. 

Holland PW (1990) Ruhin model and its application to causal inference in experiments and observational studies. American Journal of Epidemiology 132 825-826. 

Holland PW (1986) Statistics and causal inference. Journal of the American Statistical Association 81 945-960. 

In summary, one should limit the number of inferential tests to be performed to the minimum necessary for making the desired causal inferences. 

Hogan JW, Lancaster T. 2004. Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies. Slat. Methods Med. Res. 13 17-48. 

---